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Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding. (arXiv:2109.04947v3 [cs.CL] UPDATED)
May 11, 2022, 1:11 a.m. | Shane Storks, Qiaozi Gao, Yichi Zhang, Joyce Chai
cs.CL updates on arXiv.org arxiv.org
Large-scale, pre-trained language models (LMs) have achieved human-level
performance on a breadth of language understanding tasks. However, evaluations
only based on end task performance shed little light on machines' true ability
in language understanding and reasoning. In this paper, we highlight the
importance of evaluating the underlying reasoning process in addition to end
performance. Toward this goal, we introduce Tiered Reasoning for Intuitive
Physics (TRIP), a novel commonsense reasoning dataset with dense annotations
that enable multi-tiered evaluation of machines' reasoning …
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